User adaptive image compensator

ABSTRACT

A user adaptive image compensator includes a feature extractor, a compensated image generator, an image selector, and a preference parameter updater. The feature extractor extracts features from an input image. The compensated image generator generates compensated preference parameters based on a preference parameter. The compensated image generator generates a plurality of compensated images by compensating the input image based on the compensated preference parameters. The image selector displays the compensated images to a user. The image selector outputs a selected compensated image, which is selected from the compensated images by the user, as an output image. The image selector outputs a selected compensated preference parameter from the compensated preference parameters and which corresponds to the selected compensated image. The preference parameter updater updates the preference parameter based on the selected compensated preference parameter and the extracted features.

CROSS-REFERENCE TO RELATED APPLICATION

This U.S. Non-provisional application claims priority under 35 U.S.C. §119 to Korean Patent Application No. 10-2015-0105073, filed on Jul. 24,2015, in the Korean Intellectual Property Office (KIPO), the disclosureof which is incorporated by reference in its entirety herein.

BACKGROUND

Example embodiments relate generally to an image compensator, and moreparticularly to an image compensator that modifies images according to apreference of a user.

In general, image data generated by an image sensor is stored in storageafter compensation according to a universal preference designed for ageneral user or based on a policy of a company that manufactures theimage sensor. If size of the image data is greater than a size of thestorage, a down-sizing procedure or a compressing procedure may beapplied to the image data by the compensation.

Accordingly, a conventional image compensation method that is based on auniversal preference of a general user may not be a suitablecompensation of the image data according to a preference of anindividual user.

SUMMARY

At least one example embodiment of the inventive concept provides animage compensator that learns preferences of an individual user andexecutes image compensation based on the learned preferences.

At least one example embodiment of the inventive concept provides animage compensator that respectively learns preferences of individualusers of a plurality of users and executes image compensation based onthe learned preference of a current user.

According to example embodiments, a user adaptive image compensatorincludes a feature extractor, a compensated image generator, an imageselector, and a preference parameter updater. The feature extractorextracts features from an input image. The compensated image generatorgenerates compensated preference parameters based on a preferenceparameter. The compensated image generator generates a plurality ofcompensated images of the input image based on the generated compensatedpreference parameters. The image selector displays the compensatedimages to a user. The image selector outputs a selected compensatedimage, which is selected by the user from the compensated images, as anoutput image. The image selector outputs a selected compensatedpreference parameter that corresponds to the selected compensated image.The preference parameter updater updates the preference parameter basedon the selected compensated preference parameter and the extractedfeatures.

In an example embodiment, the plurality of the compensated images mayinclude first through (2N+1)-th compensated images, in which N is anatural number, and the compensated preference parameters may includefirst through (2N+1)-th compensated preference parameters. Thecompensated image generator may generate the (N+1)-th compensatedpreference parameter having a value of the preference parametercorresponding to a (N+1)-th compensation curve. The compensated imagegenerator may generate the first through the N-th compensated preferenceparameters corresponding to first through N-th compensation curves andthe (N+2)-th through the (2N+1)-th compensated preference parameterscorresponding to the (N+2)-th through the (2N+1)-th compensation curvesbased on the preference parameter. The compensated image generator maygenerate the first through the (2N+1)-th compensated images byrespectively applying the first through the (2N+1)-th compensationcurves to the input image.

In an example embodiment, a K-th dynamic range, in which K is a naturalnumber equal to or less than (2N+1), may be a ratio of a maximumintensity to a minimum intensity of data included in the K-thcompensated image and the K-th dynamic range may be proportional to K.

In an example embodiment, the K-th compensated image, in which K is anatural number equal to or less than N, may be further compensated to ablack color according to the K-th compensation curve in comparison tothe (N+1)-th compensated image as K decreases. The L-th compensatedimage, in which L is a natural number that is equal to or larger than(N+2) and that is equal to or less than (2N+1), may be furthercompensated to a white color according to the L-th compensation curve incomparison to the (N+1)-th compensation image as L increases.

In an example embodiment, if the user selects the K-th compensatedimage, in which K is a natural number equal to or less than (2N+1), theimage selector may output a compensated preference parametercorresponding to the K-th compensation curve as the selected compensatedpreference parameter, and the preference parameter updater may updatethe preference parameter as value generated by combining the preferenceparameter and the selected compensated preference parameter based on avalue of a learning speed α.

In an example embodiment, the preference parameter updater may include apreference parameter table. The preference parameter table may include aplurality of contents corresponding to combinations of features that maybe extracted from the input image as indices. The preference parametertable may store content preference parameters corresponding to theextracted features.

In an example embodiment, the preference parameter updater may store theupdated preference parameter as a first content preference parameter ofa first content corresponding to the extracted features in thepreference parameter table. The preference parameter updater may readthe first content preference parameter corresponding to the extractedfeatures from the preference parameter table and may output the firstcontent preference parameter as the preference parameter.

In an example embodiment, a type and a number of the extracted featuresmay vary according to the input image.

In an example embodiment, the extracted features may be features of aportion of the input image such as a sky, a plant, a sea, or a human, orare features based on an entirety of the input image, such asbrightness.

In an example embodiment, the preference parameter may include vectorsrepresenting a compensation curve describing a preference of the user.

According to example embodiments, a user adaptive image compensatorincludes a feature extractor, a compensated image generator, an imageselector, and a preference parameter updater. The feature extractorextracts features from an input image. The compensated image generatorgenerates compensated preference parameters based on an identificationsignal of a current user among a plurality of users and a preferenceparameter corresponding the extracted features. The compensated imagegenerator generates a plurality of compensated images by compensatingthe input image based on the compensated preference parameters. Theimage selector displays the compensated images to the current user. Theimage selector outputs a selected compensated image that is selectedfrom the compensated images by the current user, as an output image. Theimage selector outputs a selected compensated preference parameter fromthe compensated preference parameters and that corresponds to theselected compensated image. The preference parameter updater updates thepreference parameter based on the selected compensated preferenceparameter, the extracted features, and the identification signal.

In an example embodiment, the plurality of the compensated images mayinclude first through (2N+1)-th compensated images, in which N is anatural number, and the compensated preference parameters may includefirst through (2N+1)-th compensated preference parameters. Thecompensated image generator may generate the (N+1)-th compensatedpreference parameter having a value of the preference parametercorresponding to a (N+1)-th compensation curve. The compensated imagegenerator may generate the first through the N-th compensated preferenceparameters corresponding to first through the N-th compensation curvesand the (N+2)-th through the (2N+1)-th compensated preference parameterscorresponding to the (N+2)-th through the (2N+1)-th compensation curvesbased on the preference parameter. The compensated image generator maygenerate the first through the (2N+1)-th compensated images byrespectively applying the first through the (2N+1)-th compensationcurves to the input images.

In an example embodiment, a K-th dynamic range, in which K is a naturalnumber equal to or less than (2N+1), may be a ratio of a maximumintensity to a minimum intensity of data included in the K-thcompensated image, and the K-th dynamic range may be proportional to K.

In an example embodiment, the K-th compensated image, in which K is anatural number equal to or less than N, may be further compensated to ablack color according to the K-th compensation curve compared to the(N+1)-th compensated image as K decreases. The L-th compensated image,in which L is a natural number that is equal to or larger than (N+2) andthat that is equal to or less than (2N+1), may be further compensated toa white color according to the L-th compensation curve compared to the(N+1)-th compensation image as L increases.

In an example embodiment, if the current user selects the K-thcompensated image, in which K is a natural number equal to or less than(2N+1), the image selector may output a compensated preference parametercorresponding to the K-th compensation curve as the selected compensatedpreference parameter, and the preference parameter updater may updatethe preference parameter as value generated by mixing the preferenceparameter and the selected compensated preference parameter based on avalue of a learning speed α.

According to example embodiments, a user adaptive image compensatorcomprises a feature extractor, a compensated image generator, an imageselector, and a preference parameter updater. The feature extractor mayextract features from an input image. The compensated image generatormay generate at least one compensated preference parameter based on acurrent preference parameter, and the compensated image generator maygenerate a plurality of compensated images of the input image based onthe at least one generated compensated preference parameter. The imageselector may display the plurality of compensated images and outputs acompensated image that has been selected by a user as an output imagefrom the plurality of displayed compensated images. The image selectormay also output a compensated preference parameter that corresponds tothe selected compensated image. The preference parameter updater mayupdate the current preference parameter based on the extracted featuresand the compensated preference parameter that corresponds to theselected compensated image.

In an example embodiment, the plurality of the compensated images mayinclude first through (2N+1)-th compensated images, in which N is anatural number; the compensated preference parameters may include firstthrough (2N+1)-th compensated preference parameters; the compensatedimage generator may further generate the (N+1)-th compensated preferenceparameter having a value of the preference parameter corresponding to a(N+1)-th compensation curve; the compensated image generator may furthergenerate a first through an N-th compensated preference parameterscorresponding to a first through an N-th compensation curves and an(N+2)-th through an (2N+1)-th compensated preference parametersrespectively corresponding to the (N+2)-th through (2N+1)-thcompensation curves based on the preference parameter; and thecompensated image generator may further generate the first through(2N+1)-th compensated images by respectively applying the first through(2N+1)-th compensation curves to the input image.

In an example embodiment, a K-th dynamic range, in which K is a naturalnumber equal to or less than (2N+1), may be a ratio of a maximumintensity to a minimum intensity of data included in the K-thcompensated image, and the K-th dynamic range may be proportional to K.

In example embodiments, a K-th compensated image, in which K is anatural number equal to or less than N, may be further compensated to ablack color according to the K-th compensation curve in comparison tothe (N+1)-th compensated image as K decreases, and an L-th compensatedimage, in which L is a natural number that is equal to or greater than(N+2) and that is equal to or less than (2N+1), may be furthercompensated to a white color according to the L-th compensation curvecompared to the (N+1)-th compensation image as L increases.

In example embodiments, if the user selects a K-th compensated image, inwhich K is a natural number equal to or less than (2N+1), the imageselector may further output a compensated preference parametercorresponding to the K-th compensation curve as the selected compensatedpreference parameter, and the preference parameter updater may furtherupdate the current preference parameter to be a value generated bycombining the preference parameter and the selected compensatedpreference parameter based on a learning speed value α.

In example embodiments, the preference parameter updater may include apreference parameter table; the preference parameter table may include aplurality of content entries corresponding to combinations of featuresthat may be extracted from the input image as indices; and thepreference parameter table may store stores content preferenceparameters corresponding to the extracted features.

In example embodiments, the preference parameter updater may furtherstore the updated preference parameter as a first content preferenceparameter of a first content entry corresponding to the extractedfeatures in the preference parameter table, and the preference parameterupdater may further read the first content preference parametercorresponding to the extracted features from the preference parametertable and output the first content preference parameter as the currentpreference parameter.

In example embodiments, a type and a number of the extracted featuresmay vary according to the input image.

In example embodiments, the extracted features may be features of aportion of the input image, or are features based on an entirety of theinput image.

In example embodiments, the preference parameter may include vectorsrepresenting a compensation curve that is based on a preference of theuser.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments of the inventive concept will be more clearlyunderstood from the following detailed description taken in conjunctionwith the accompanying drawings.

FIG. 1 is a block diagram illustrating a user adaptive image compensatoraccording to an example embodiment.

FIGS. 2A and 2B are diagrams illustrating an input image of FIG. 1 andfeatures included in the input image.

FIGS. 3A and 3B are graphs respectively illustrating an (N+1)-thcompensation curve of FIG. 1 and preference parameters corresponding tothe (N+1)-th compensation curve.

FIGS. 4A and 4B are diagrams illustrating example embodiments of apreference parameter table of the preference parameter updater includedin the user adaptive image compensator of FIG. 1.

FIGS. 5A and 5B are graphs illustrating compensation curves applied tothe compensated images of FIG. 1 according to an example embodiment.

FIGS. 6A and 6B are graphs illustrating compensation curves applied tothe compensated images of FIG. 1 according to another exampleembodiment.

FIG. 7 is a diagram illustrating operation of the preference parameterupdater included in the user adaptive image compensator of FIG. 1.

FIG. 8 is a block diagram illustrating a user adaptive image compensatoraccording to another example embodiment.

FIGS. 9A and 9B are diagrams illustrating example embodiments of thepreference parameter table of the preference parameter updater includedin the user adaptive image compensator of FIG. 8.

FIG. 10 is a block diagram illustrating a computing system according toan example embodiment.

FIG. 11 is a block diagram illustrating an example embodiment ofinterface used in the computing system of FIG. 10.

FIG. 12 is a block diagram illustrating a mobile system according to anexample embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Various example embodiments will be described more fully hereinafterwith reference to the accompanying drawings, in which some exampleembodiments are shown. The present inventive concept may, however, beembodied in many different forms and should not be construed as limitedto the example embodiments set forth herein. Rather, these exampleembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the present inventiveconcept to those skilled in the art. In the drawings, the sizes andrelative sizes of layers and regions may be exaggerated for clarity.Like numerals refer to like elements throughout.

It will be understood that, although the terms first, second, third etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are used to distinguish oneelement from another. Thus, a first element discussed below could betermed a second element without departing from the teachings of thepresent inventive concept. As used herein, the term “and/or” includesany and all combinations of one or more of the associated listed items.

It will be understood that when an element is referred to as being“connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between” versus “directly between,” “adjacent” versus “directlyadjacent,” etc.).

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting of thepresent inventive concept. As used herein, the singular forms “a,” “an”and “the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will be further understood thatthe terms “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted in the blocks may occur out of the order noted inthe flowcharts. For example, two blocks shown in succession may in factbe executed substantially concurrently or the blocks may sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this inventive concept belongs. Itwill be further understood that terms, such as those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

FIG. 1 is a block diagram illustrating a user adaptive image compensatoraccording to an example embodiment.

Referring to FIG. 1, the user adaptive image compensator 100 includes afeature extractor 110, a compensated image generator 120, an imageselector 130, and a preference parameter updater 140.

The feature extractor 110 extracts features (FEATURES) from an inputimage (INPUT IMAGE (II)). In an example embodiment, the extractedfeatures may be features of a portion of the input image II, such as asky, a plant, a sea, or a human. In this case, the feature extractor 110may store masks corresponding to pre-defined features. The featureextractor 110 may select one mask among the stored masks and maydetermine whether the input image II includes a feature corresponding tothe selected mask based on the result of convolution of the selectedmask and the input image II. If the convolution is done for all masks,the feature extractor 110 may determine whether the input image IIincludes features corresponding to any of the respective masks. Inanother example embodiment, the extracted features may be featuresrelating to the entirety of the input image II, such as brightness. Inthis case, the feature extractor 110 may extract an average value ofluminance as brightness for the pixel data included in the entire inputimage II.

The feature extractor 110 may alternatively extract the featuresincluded in the input image II by another method than the methoddescribed herein. In an example embodiment, the type and number of theextracted features may vary according to the input image II.

The extracted features from the input image II will be described withthe references to FIGS. 2A and 2B.

The compensated image generator 120 generates compensated preferenceparameters (COMPENSATED PREFERENCE PARAMETERS (CPP)) based on apreference parameter (PREFERENCE PARAMETER (PP)). The compensated imagegenerator 120 generates a plurality of compensated images (COMPENSATEDIMAGES (CI)) by compensating the input image II based on the compensatedpreference parameters CPP.

The plurality of the compensated images CI may include first through(2N+1) compensated images, in which N is a natural number. Thecompensated preference parameters CPP may include first through (2N+1)compensated preference parameters. The compensated image generator 120may generate the (N+1) compensated preference parameters having a valueof the preference parameter PP corresponding to a (N+1)-th compensationcurve. The compensated image generator 120 may generate the firstthrough the N-th compensated preference parameters corresponding tofirst through the N-th compensation curves and the (N+2)-th through the(2N+1)-th compensated preference parameters corresponding to the(N+2)-th through the (2N+1)-th compensation curves based on thepreference parameter PP.

In a first example embodiment, a K-th dynamic range, in which K is anatural number equal to or less than (2N+1), may be a ratio of a maximumintensity to a minimum intensity of data included in the K-thcompensated image, and in which the K-th dynamic range may beproportional to K. The first example embodiment will be described withthe references to FIGS. 8 and 9. In another example embodiment, the K-thdynamic range may be inversely proportional to K.

In a second example embodiment, the L-th compensated image, in which Lis a natural number equal to or less than N, may be further compensatedto a black color according to the L-th compensation curve compared tothe (N+1)-th compensated image as L decreases. The M-th compensatedimage, in which M is a natural number that is equal to or greater than(N+2) and that is equal to or less than (2N+1), may be furthercompensated to a white color according to the M-th compensation curvecompared to the (N+1)-th compensation image as M increases. The secondexample embodiment will be described with the references to FIGS. 10 and11. In another embodiment, color direction of the compensation may beopposite to the description provided herein, that is, in the colordirection operations described, the black color and the white color maybe exchanged.

The image selector 130 displays the compensated images CI to a user. Theimage selector 130 outputs a selected compensated image, which isselected by the user and output as an output image (OUTPUT IMAGE (OI)).The image selector 130 outputs a selected compensated preferenceparameter (SELECTED COMPENSATED PREFERENCE PARAMETER (SCPP)), whichcorresponds to the selected compensated image among the compensatedpreference parameters CPP. For example, if the user selects the K-thcompensated image, in which K is a natural number equal to or less than(2N+1), the image selector 130 may output a compensated preferenceparameter corresponding to the K-th compensation curve as the selectedcompensated preference parameter SCPP and may output the K-thcompensated image as the output image OI.

The preference parameter updater 140 updates the preference parameter PPbased on the selected compensated preference parameter SCPP and theextracted features. The preference parameter updater 140 may update thepreference parameter PP as a value generated by combining the preferenceparameter PP and the selected compensated preference parameter SCPPbased on a value of a learning speed α, which is described below.

Operation of the image selector 130 and the preference parameter updater140 will be described with the reference to FIG. 7.

FIGS. 2A and 2B are diagrams illustrating the input image of FIG. 1 andfeatures included in the input image.

FIG. 2A shows a situation in which the feature extractor 110 extracts asky feature SKY and a flower feature FLOWER from a first input imageINPUT IMAGE 1. FIG. 2B shows a situation in which the feature extractor110 extracts a sky feature SKY, a face feature FACE, and a plant featurePLANT from a second input image INPUT IMAGE 2.

FIGS. 3A and 3B are graphs respectively illustrating an (N+1)-thcompensation curve of FIG. 1 and preference parameters corresponding tothe (N+1)-th compensation curve.

Referring to FIGS. 3A and 3B, the (N+1)-th compensation curvecorresponds to the preference parameter PP. The (N+1)-th compensationcurve represents a relationship between the input image II havingintensity between 0 and A, and the compensated images CI havingintensity between 0 and S*A. S is a scaling ratio. If S is greater than1, the (N+1)-th compensation curve may have an up-sizing characteristic.If S is less than 1, the (N+1)-th compensation curve may have adown-sizing characteristic.

In FIG. 3A, the (N+1)-th compensation curve is represented as fivevectors (VA1, VA2, VA3, VA4, and VA5). In FIG. 3B, the (N+1)-thcompensation curve is represented as seven vectors VB1, VB2, VB3, VB4,VB5, VB6, and VB7. The (N+1)-th compensation curve may be represented asless than 5 vectors or more than 7 vectors.

The first through the N-th compensation curves and the (N+2)-th throughthe (2N+1)-th compensation curves may be understood based on thedescription of the (N+1)-th compensation curve. In an exampleembodiment, an initial preference parameter PP may be selected as auniversal preference parameter for general users.

FIGS. 4A and 4B are diagrams illustrating example embodiments of apreference parameter table of the preference parameter updater includedin the user adaptive image compensator of FIG. 1.

Referring to FIG. 4A, a preference parameter table 141A may include aplurality of contents CONTENTS corresponding to combinations of featuresFEATURES that may be extracted from the input image II as indices. FIGS.4A and 4B show a situation in which the preference parameter table 141includes a first content entry and a second content entry as indices.The first content corresponds to a combination of the sky feature SKYand the flower feature FLOWER. The second content corresponds to acombination of the sky feature SKY, the face feature FACE, and the plantfeature PLANT.

In FIG. 4A, the preference parameter table 141A may store a firstcontent preference parameter P₁ corresponding to the first contententry. The preference parameter table 141 may store a second contentpreference parameter P₂ corresponding to the second content entry. Thepreference parameter table 141A may store additional content preferenceparameters other than the first and second content preference parametersP₁ and P₂ that are depicted in FIG. 4A.

The preference parameter updater 140 may store an updated preferenceparameter as a content preference parameter of a content correspondingto the extracted features in the preference parameter table 141. Thepreference parameter updater 140 may read the content preferenceparameter corresponding to the extracted features from the preferenceparameter table 141 and may output the content preference parameter asthe preference parameter PP.

In FIG. 4B, the preference parameter table 141B may store the skyfeature SKY included in a first content entry as a first content skyfeature preference parameter P_(1S) and may store the flower featureFLOWER included in the first content entry as a first content flowerfeature preference parameter P_(1F). The first content sky featurepreference parameter P_(1S) and the first content flower featurepreference parameter P_(1F) of FIG. 4B may correspond to the firstcontent entry preference parameter P₁ of FIG. 4A. The preferenceparameter table 141B may store the sky feature SKY included in thesecond content entry as a second content sky feature preferenceparameter P_(2S), may store the face feature FACE included in the secondcontent entry as the second content face feature preference parameterP_(2F), and may store the plant feature PLANT included in the secondcontent entry as the second content plant feature preference parameterP_(2P). The second content sky feature preference parameter P_(1S), thesecond content face feature preference parameter P_(2F), and the secondcontent plant feature preference parameter P_(2P) may correspond to thesecond content preference parameter P₂ of FIG. 4A.

FIGS. 5A and 5B are graphs illustrating compensation curves applied tothe compensated images of FIG. 1 according to an example embodiment.

FIGS. 5A and 5B shows a case in which N is 2, the K-th dynamic range, inwhich K is a natural number equal to or less than 5, is a ratio ofmaximum intensity to minimum intensity of data included in the K-thcompensated image and in which the K-th dynamic range is proportional toK. In another example embodiment, N may be a natural number other than2.

Referring to FIG. 5A, the compensated image generator 120 may generatethe fourth compensated preference parameter corresponding to the fourthcompensation curve CURVE_A2 and the fifth compensated preferenceparameter corresponding to the fifth compensation curve CURVE_A1 basedon the third compensated preference parameter having value of thepreference parameter PP corresponding to the third compensation curveCURVE_ORIG.

The compensated image generator 120 may generate a compensated imageincluding data having a maximum intensity OUB_ORIG and a minimumintensity OLB_ORIG by compensating the input image II including datahaving a maximum intensity IUB1 and a minimum intensity ILB1 accordingto the third compensation curve CURVE_ORIG. The compensated imagegenerator 120 may generate a compensated image including a data having amaximum intensity OUB1 and a minimum intensity OLB1 by compensating theinput image II including data having a maximum intensity IUB1 and aminimum intensity ILB1 according to the fifth compensation curveCURVE_A1. The fourth compensation curve CURVE_A2 existing between thethird compensation curve CURVE_ORIG and the fifth compensation curveCURVE_A1 may be understood based on a similar description. The fifthdynamic range (OUB1/OLB1) is greater than the fourth dynamic range andthe fourth dynamic range is greater than the third dynamic range(OUB_ORIG/OLB_ORIG).

Referring to FIG. 5B, the compensated image generator 120 may generatethe first compensated preference parameter corresponding to the firstcompensation curve CURVE_B1 and the second compensated preferenceparameter corresponding to the second compensation curve CURVE_B2 basedon the third compensated preference parameter having value of thepreference parameter PP corresponding to the third compensation curveCURVE_ORIG.

The compensated image generator 120 may generate a compensated imageincluding data having a maximum intensity OUB_ORIG and a minimumintensity OLB_ORIG by compensating the input image II including a datahaving a maximum intensity IUB2 and a minimum intensity ILB2 accordingto the third compensation curve CURVE_ORIG. The compensated imagegenerator 120 may generate a compensated image including data having amaximum intensity OUB2 and a minimum intensity OLB2 by compensating theinput image II including a data having a maximum intensity IUB2 and aminimum intensity ILB2 according to the first compensation curveCURVE_B1. The second compensation curve CURVE_B2 existing between thefirst compensation curve CURVE_B1 and the third compensation curveCURVE_ORIG may be understood based on a similar description. The firstdynamic range (OUB2/OLB2) is less than the second dynamic range and thesecond dynamic range is less than the third dynamic range(OUB_ORIG/OLB_ORIG).

Referring to FIGS. 5A and 5B, the compensated image generator 120 maygenerate the first through the fifth compensated images by respectivelyapplying the first through the fifth compensation curves CURVE_B1,CURVE_B2, CURVE_ORIG, CURVE_A2, and CURVE_A1 to the input image II. As aresult, if the input image II is compensated to the first compensatedimage, the first compensated image has the smallest dynamic range and ofthe input image II is compensated to the fifth compensated image, thefifth compensated image has the largest dynamic range.

FIGS. 6A and 6B are graphs illustrating compensation curves applied tothe compensated images of FIG. 1 according to another exampleembodiment.

FIGS. 6A and 6B shows a case in which N is 2, the K-th compensatedimage, in which K is a natural number equal to or less than 5, isfurther compensated to a black color according to the K-th compensationcurve as K decreases, and the K-th compensated image is furthercompensated to a white color according to the K-th compensation curve asK increases. In another example embodiment, N may be a natural numberother than 2.

Referring to FIG. 6A, the compensated image generator 120 may generatethe fourth compensated preference parameter corresponding to the fourthcompensation curve CURVE_C2 and the fifth compensated preferenceparameter corresponding to the fifth compensation curve CURVE_C1 basedon the third compensated preference parameter having value of thepreference parameter PP corresponding to the third compensation curveCURVE_ORIG.

The compensated image generator 120 may generate a compensated imageincluding data having a maximum intensity OUB_ORIG and a minimumintensity OLB_ORIG by compensating the input image II including datahaving a maximum intensity IUB3 and a minimum intensity ILB3 accordingto the third compensation curve CURVE_ORIG. The compensated imagegenerator 120 may generate a compensated image including a data having amaximum intensity OUB3 and a minimum intensity OLB3 by compensating theinput image II including data having a maximum intensity IUB3 and aminimum intensity ILB3 according to the fifth compensation curveCURVE_C1. The fourth compensation curve CURVE_C2 existing between thethird compensation curve CURVE_ORIG and the fifth compensation curveCURVE_C1 may be understood based on a similar description. The fifthcompensation curve CURVE_C1 is closer to a white color than the fourthcompensation curve CURVE_C2, and the fourth compensation curve CURVE_C2is closer to a white color than the third compensation curve CURVE_ORIG.

Referring to FIG. 6B, the compensated image generator 120 may generatethe first compensated preference parameter corresponding to the firstcompensation curve CURVE_D1 and the second compensated preferenceparameter corresponding to the second compensation curve CURVE_D2 basedon the third compensated preference parameter having value of thepreference parameter PP corresponding to the third compensation curveCURVE_ORIG.

The compensated image generator 120 may generate a compensated imageincluding data having a maximum intensity OUB_ORIG and a minimumintensity OLB_ORIG by compensating the input image II including datahaving a maximum intensity IUB4 and a minimum intensity ILB4 accordingto the third compensation curve CURVE_ORIG. The compensated imagegenerator 120 may generate a compensated image including data having amaximum intensity OUB4 and a minimum intensity OLB4 by compensating theinput image II including data having a maximum intensity IUB4 and aminimum intensity ILB4 according to the first compensation curveCURVE_D1. The second compensation curve CURVE_D2 existing between thefirst compensation curve CURVE_D1 and the third compensation curveCURVE_ORIG may be understood based on a similar description. The firstcompensation curve CURVE_D1 is closer to a black color than the secondcompensation curve CURVE_D2, and the second compensation curve CURVE_D2is closer to a black color than the third compensation curve CURVE_ORIG.

Referring to FIGS. 6A and 6B, the compensated image generator 120 maygenerate the first through the fifth compensated images by respectivelyapplying the first through the fifth compensation curves CURVE_D1,CURVE_D2, CURVE_ORIG, CURVE_C2, and CURVE_C1 to the input image II. As aresult, if the input image II is compensated to the first compensatedimage, the first compensated image is closest to a black color and ifthe input image II is compensated to the fifth compensated image, fifthcompensated image is closest to a white color.

FIG. 7 is a diagram illustrating operation of the preference parameterupdater included in the user adaptive image compensator of FIG. 1.

FIG. 7 shows operation of the compensated image generator 120 and thepreference parameter updater 140 when the (T−1)-th input image havingthe sky feature SKY and the flower feature FLOWER and T-th input imagehaving the sky feature SKY and the flower feature FLOWER aresequentially inputted to the compensated image generator 120 and N is 1.

When the (T−1)-th input image is inputted to the compensated imagegenerator 120, the preference parameter updater 140 outputs the firstcontent preference parameter P1 of the first content corresponding tothe sky feature SKY and the flower feature FLOWER according to thepreference parameter table 141A as the preference parameter PP.

The compensated image generator 120 generates the second compensatedpreference parameter P₁(2, T−1) having the preference parameter PP andgenerates the first and third compensated preference parameters P₁(1,T−1) and P₁(3, T−1) by modifying the preference parameter PP.

In an example embodiment, the first through third compensation curvescorresponding to the first through third compensated preferenceparameters P₁(1, T−1), P₁(2, T−1), and P₁(3, T−1) may be respectivelygenerated by changing the dynamic range changing as described inreference to FIGS. 5A and 5B. In another example embodiment, the firstthrough the third compensation curves corresponding to the first throughthe third compensated preference parameters P₁(1, T−1), P₁(2, T−1), andP₁(3, T−1) may be generated by shifting the color as described inreference to FIGS. 6A and 6B. In still another example embodiment, thefirst through the third compensation curves corresponding to the firstthrough the third compensated preference parameters P₁(1, T−1), P₁(2,T−1), and P₁(3, T−1) may be generated by another technique that isdifferent from the technique described in reference to FIGS. 5A through6B.

The compensated image generator 120 may generate the first compensatedimage CI(1, T−1) by applying the first compensated preference parameterP₁(1, T−1) to the (T−1)-th input image. The compensated image generator120 may generate the second compensated image CI(2, T−1) by applying thesecond compensated preference parameter P₁(2, T−1) to the (T−1)-th inputimage. The compensated image generator 120 may generate the thirdcompensated image CI(3, T−1) by applying the third compensatedpreference parameter P₁(3, T−1) to the (T−1)-th input image.

If the user selects the third compensated image CI(3, T−1), the imageselector 130 outputs the third compensated preference parameter P₁(3,T−1) corresponding to the third compensated image CI(3, T−1) as theselected compensated preference parameter SCPP. The preference parameterupdater 140 updates the preference parameter P₁(2, T) as value generatedby combining the preference parameter P₁(2, T−1) and the selectedcompensated preference parameter P₁(3, T−1) according to Equation 1based on a value of a learning speed α which is between 0 and 1. If thevalue of the learning speed α is close to 0, the changing rate of thepreference parameter PP is slow. If the value of the learning speed α isclose to 1, the changing rate of the preference parameter PP is fast.P ₁ =α*P ₁(2,T−1)+(1−α)*P ₁(3,T−1)  (1)

The preference parameter updater 140 stores the combined value P₁generated by Equation 1 as the first content preference parameter P₁ ofthe preference parameter table 141A of FIG. 4A.

When the T-th input image is input to the compensated image generator120, the preference parameter updater 140 outputs the first contentpreference parameter P₁ of the first content corresponding to the skyfeature SKY and the flower feature FLOWER according to the preferenceparameter table 141A as the preference parameter PP.

The compensated image generator 120 generates the fifth compensatedpreference parameter P₂(2, T) having the preference parameter PP andgenerates the fourth and sixth compensated preference parameters P₂(1,T) and P₂(3, T)P2(3, T) by modifying the preference parameter PP. Atechnique of generating the fourth through sixth compensated preferenceparameters P₂(1, T), P₂(2, T), and P₂(3, T) may be understood based onthe preceding description.

The compensated image generator 120 may generate the fourth compensatedimage CI(1, T) by applying the fourth compensated preference parameterP₂(1, T)P2(1, T) to the T-th input image. The compensated imagegenerator 120 may generate the fifth compensated image CI(2, T) byapplying the fifth compensated preference parameter P₂(2, T) to the T-thinput image. The compensated image generator 120 may generate the sixthcompensated image CI(3, T) by applying the sixth compensated preferenceparameter P₂(3, T) to the T-th input image. The user may select oneamong the fourth through sixth compensated images CI(1, T), CI(2, T),and CI(3, T). Subsequent operations may repeat the operations that havebeen described.

FIG. 8 is a block diagram illustrating a user adaptive image compensatoraccording to another example embodiment.

Referring to FIG. 8, a user adaptive image compensator 200 includes afeature extractor 210, a compensated image generator 220, an imageselector 230, and a preference parameter updater 240.

The feature extractor 210 extracts features FEATURES from an input imageINPUT IMAGE (II). The compensated image generator 220 generatescompensated preference parameters (COMPENSATED PREFERENCE PARAMETERS(CPP)) based on an identification signal USER ID SIGNAL (UIS) of acurrent user among a plurality of users and a preference parameterPREFERENCE PARAMETER (PP) corresponding the extracted features. Thecompensated image generator 220 generates a plurality of compensatedimages COMPENSATED IMAGES (CI) by compensating the input image II basedon the compensated preference parameters CPP. The image selector 230displays the compensated images CI to the current user. The imageselector 230 outputs a selected compensated image, which is selectedamong the compensated images CI by the current user, as an output imageOUTPUT IMAGE (OI). The image selector 230 outputs a selected compensatedpreference parameter SCPP, which corresponds to the selected compensatedimage, among the compensated preference parameters CPP. The preferenceparameter updater 240 updates the preference parameter PP based on theselected compensated preference parameter SCPP, the extracted features,and the identification signal UIS.

The preference parameter updater 240 may include a preference parametertable 241. The preference parameter table 241 will be described with thereferences to FIGS. 9A and 9B.

FIGS. 9A and 9B are diagrams illustrating example embodiments of thepreference parameter table of the preference parameter updater includedin the user adaptive image compensator of FIG. 8.

Referring to FIG. 9A, the preference parameter table 241A may include aplurality of content entries CONTENTS corresponding to features that maybe extracted from the input image II and the identification signal UISof the users as indices. FIG. 9A shows a situation in which thepreference parameter table 241A includes two content entries for each oftwo users. In an example embodiment, the preference parameter table 241Amay include more content entries for each user and may include moreusers than two. In another example embodiment, the preference parametertable 241A may include fewer content entries for each users and mayinclude fewer users than two.

In FIG. 9A, the preference parameter table 241A may include a first usercontent preference parameter P₁₁ corresponding to a first user and afirst content entry corresponding to a combination of the sky featureSKY and the flower feature FLOWER. The preference parameter table 241Amay include a second user content preference parameter P₁₂ correspondingto the first user and a second content entry corresponding to acombination of the sky feature SKY, the face feature FACE, and the plantfeature PLANT. The preference parameter table 241A may include a thirduser content preference parameter P₂₁ corresponding to a second user andthe first content entry corresponding to a combination of the skyfeature SKY and the flower feature FLOWER. The preference parametertable 241A may include a fourth user content preference parameter P₂₂corresponding to the second user and the second content entrycorresponding to a combination of the sky feature SKY, the face featureFACE, and the plant feature PLANT. The preference parameter table 241Amay include additional users, content entries, and user contentpreference parameters that are not shown.

The preference parameter updater 240 may store an updated preferenceparameter as a user content preference parameter corresponding to acombination of the identification signal UIS and a content correspondingto the extracted features in the preference parameter table 241A. Thepreference parameter updater 240 may read the user content preferenceparameter corresponding to the extracted features and the identificationsignal UIS from the preference parameter table 241A, and output the usercontent preference parameter as the preference parameter PP.

Referring to FIG. 9B, the preference parameter table 241B may include aplurality of content entries corresponding to features that may beextracted from the input image II and the identification signal UIS ofthe users as indices. In particular, FIG. 9B shows a configuration inwhich the preference parameter table 241B includes more than two contententries for each of two users and in which the content entries arecombinations of features that are extracted from the input image II. Inan example embodiment, the preference parameter table 241B may includemore content entries for each user than depicted in FIG. 9B and mayinclude entries for more than two users. In another example embodiment,the preference parameter table 241B may include fewer content entriesfor each user and may include fewer than two users.

In FIG. 9B, for a first user, the preference parameter table 241B mayinclude a first user content preference parameter P_(11S) for a firstcontent entry corresponding to a sky feature SKY and a second usercontent preference parameter P_(11F) for a second content entrycorresponding to a flower feature FLOWER. The preference parameter table241B may also include for the first user a third user content preferenceparameter P_(12S) corresponding to the sky feature SKY, a fourth usercontent preference parameter P_(12F) corresponding to the face featureFACE, and a fifth user content preference parameter P_(12P)corresponding to the plant feature PLANT. For a second user, thepreference parameter table 241A may include a first user contentpreference parameter P_(21S) corresponding to the sky feature SKY, asecond user content preference parameter P_(22F) corresponding to theflower feature FLOWER. The preference table 241B may also include forthe second user a third user content preference parameter P_(22S)corresponding to the sky feature SKY, a fourth user content preferenceparameter P_(22F) corresponding to the face feature FACE, and a fifthuser content preference parameter P_(22P) corresponding to the plantfeature PLANT.

The preference parameter updater 240 may store an updated preferenceparameter as a user content preference parameter corresponding to acombination of the identification signal UIS and a content correspondingto the extracted features in the preference parameter table 241B. Thepreference parameter updater 240 may read the user content preferenceparameter corresponding to the extracted features and the identificationsignal UIS from the preference parameter table 241B, and output the usercontent preference parameter as the preference parameter PP.

FIG. 10 is a block diagram illustrating a computing system according toan example embodiment.

Referring to FIG. 10, a computing system 300 may include an image sensor310, a processor 320 and a storage device 330.

The image sensor 310 may generate a digital signal corresponding to anincident light. In an example embodiment, the image sensor 310 mayinclude an image compensator IC generating the digital signal bycompensating a signal generated from the incident light. The imagecompensator IC may be implemented as one of the user adaptive imagecompensators 100 and 200 of FIGS. 1 and 8. In another exampleembodiment, the image compensator IC may be implemented in the processor320 to compensate an image stored in the storage device 330. The storagedevice 330 may store the digital signal. The processor 320 may controloperations of the image sensor 310 and the storage device 330.

The computing system 300 may further include a memory device 340, aninput/output device 350 and a power supply 360. Although it is notillustrated in FIG. 10, the computing system 300 may further includeports that communicate with a video card, a sound card, a memory card, auniversal serial bus (USB) device, or other electronic devices.

The processor 320 may perform various calculations or tasks. Accordingto some embodiments, the processor 320 may be a microprocessor or a CPU.The processor 320 may communicate with the storage device 330, thememory device 340 and the input/output device 350 via an address bus, acontrol bus, and/or a data bus. In some example embodiments, theprocessor 320 may be coupled to an extended bus, such as a peripheralcomponent interconnection (PCI) bus.

The storage device 330 may include a non-volatile memory device, such asa flash memory device, a solid-state drive (SSD), a hard disk drive(HDD), a compact disk read-only memory (CD-ROM) drive, etc.

The memory device 340 may store data required for an operation of theelectronic device 300. The memory device 340 may be a dynamic randomaccess memory (DRAM), a static random access memory (SRAM), or anon-volatile memory, such as an erasable programmable read-only memory(EPROM), an electrically erasable programmable read-only memory(EEPROM), a flash memory, etc.

The input/output device 350 may include a keyboard, a mouse, a printer,a display device, etc. The power supply 360 may supply operationalpower.

The image sensor 310 may include a pixel array that detects incidentlight to generate an analog signal, and an analog-digital conversionunit that performs a sigma-delta analog-digital conversion and a cyclicanalog-digital conversion with respect to the analog signal to generatea digital signal in a first operation mode and performs a single-slopeanalog-digital conversion with respect to the analog signal to generatethe digital signal in a second operation mode.

The image sensor 310 may be packaged in various forms, such as packageon package (PoP), ball grid arrays (BGAs), chip scale packages (CSPs),plastic leaded chip carrier (PLCC), plastic dual in-line package (PDIP),die in waffle pack, die in wafer form, chip on board (COB), ceramic dualin-line package (CERDIP), plastic metric quad flat pack (MQFP), thinquad flat pack (TQFP), small outline IC (SOIC), shrink small outlinepackage (SSOP), thin small outline package (TSOP), system in package(SIP), multi chip package (MCP), wafer-level fabricated package (WFP),or wafer-level processed stack package (WSP).

According to example embodiments, the image sensor 310 may be integratedwith the processor 320 in one chip, or the image sensor 310 and theprocessor 320 may be implemented as separate chips.

The computing system 300 may be any computing system using an imagesensor. For example, the computing system 300 may include a digitalcamera, a mobile phone, a smart phone, a portable multimedia player(PMP), a personal digital assistant (PDA), etc.

FIG. 11 is a block diagram illustrating an example embodiment ofinterface used in the computing system of FIG. 10.

Referring to FIG. 11, a computing system 400 may be implemented by adata processing device (e.g., a cellular phone, a personal digitalassistant, a portable multimedia player, a smart phone, etc.) that usesor supports a mobile industry processor interface (MIPI) interface. Thecomputing system 400 may include an application processor 410, an imagesensor 440, a display device 450, etc.

A CSI host 412 of the application processor 410 may perform a serialcommunication with a CSI device 441 of the image sensor 440 via a cameraserial interface (CSI). In some embodiments, the CSI host 412 mayinclude a deserializer (DES), and the CSI device 441 may include aserializer (SER). In an example embodiment, the image sensor 440 mayinclude an image compensator IC generating the digital signal bycompensating a signal generated from the incident light. The imagecompensator IC may be implemented as one of the user adaptive imagecompensators 100 and 200 of FIGS. 1 and 8. In another exampleembodiment, the image compensator IC may be implemented in theapplication processor 410 to compensate an image stored in the storagedevice 470.

A DSI host 411 of the application processor 410 may perform a serialcommunication with a DSI device 451 of the display device 450 via adisplay serial interface (DSI). In some example embodiments, the DSIhost 411 may include a serializer (SER), and the DSI device 451 mayinclude a deserializer (DES).

The computing system 400 may further include a radio frequency (RF) chip460 performing a communication with the application processor 410. Aphysical layer (PHY) 413 of the computing system 400 and a physicallayer (PHY) 461 of the RF chip 460 may perform data communications basedon a MIPI DigRF. The application processor 410 may further include aDigRF MASTER 414 that controls the data communications according to theMIPI DigRF of the PHY 461, and the RF chip 460 may further include aDigRF SLAVE 462 controlled by the DigRF MASTER 414.

The computing system 400 may further include a global positioning system(GPS) 420, the storage device 470, a MIC 480, a DRAM device 485, and aspeaker 490. In addition, the computing system 400 may performcommunications using an ultra wideband (UWB) 510, a wireless local areanetwork (WLAN) 520, a worldwide interoperability for microwave access(WIMAX) 530, etc. However, the structure and the interface of thecomputing system 400 are not limited thereto.

FIG. 12 is a block diagram illustrating a mobile system according to anexample embodiment.

Referring to FIG. 12, a computing system 600 includes a processor 610,an input/output hub (IOH) 620, an input/output controller hub (ICH) 630,at least one memory module 640, a network device 660 and a graphics card650. In some embodiments, the computing system 600 may be a personalcomputer (PC), a server computer, a workstation, a laptop computer, amobile phone, a smart phone, a personal digital assistant (PDA), aportable multimedia player (PMP), a digital camera), a digitaltelevision, a set-top box, a music player, a portable game console, anavigation system, etc.

The processor 610 may perform various computing functions, such asexecuting specific software for performing specific calculations ortasks. For example, the processor 610 may be a microprocessor, a centralprocess unit (CPU), a digital signal processor, or the like. In someembodiments, the processor 610 may include a single core or multiplecores. For example, the processor 610 may be a multi-core processor,such as a dual-core processor, a quad-core processor, a hexa-coreprocessor, etc. Although FIG. 12 illustrates the computing system 600including one processor 610, in some embodiments, the computing system600 may include a plurality of processors.

The processor 610 may include a memory controller for controllingoperations of the memory module 640. The memory controller included inthe processor 610 may be referred to as an integrated memory controller(IMC). A memory interface between the memory controller and the memorymodule 640 may be implemented with a single channel including aplurality of signal lines, or may be implemented with multiple channels,to each of which at least one memory module 640 may be coupled. In someembodiments, the memory controller may be located inside theinput/output hub 620. The input/output hub 620 including the memorycontroller may be referred to as memory controller hub (MCH). Theprocessor 610 may include an image compensator UAIC to compensate animage stored in the memory module 640. The image compensator UAIC may beimplemented as one of the user adaptive image compensators 100 and 200of FIGS. 1 and 8.

The memory module 640 may include a plurality of memory devices MEM 641that store data provided from the memory controller.

The input/output hub 620 may manage data transfer between processor 610and devices, such as the graphics card 650. The input/output hub 620 maybe coupled to the processor 610 via various interfaces. For example, theinterface between the processor 610 and the input/output hub 620 may bea front side bus (FSB), a system bus, a HyperTransport, a lightning datatransport (LDT), a QuickPath interconnect (QPI), a common systeminterface (CSI), etc. The input/output hub 620 may provide variousinterfaces with the devices. For example, the input/output hub 620 mayprovide an accelerated graphics port (AGP) interface, a peripheralcomponent interface-express (PCIe), a communications streamingarchitecture (CSA) interface, etc. Although FIG. 12 illustrates thecomputing system 600 including one input/output hub 620, in someembodiments, the computing system 600 may include a plurality ofinput/output hubs.

The graphics card 650 may be coupled to the input/output hub 620 via AGPor PCIe. The graphics card 650 may control a display device fordisplaying an image. The graphics card 650 may include an internalprocessor for processing image data and an internal memory device. Insome embodiments, the input/output hub 620 may include an internalgraphics device along with or instead of the graphics card 650 outsidethe graphics card 650. The graphics device included in the input/outputhub 620 may be referred to as integrated graphics. Further, theinput/output hub 620 including the internal memory controller and theinternal graphics device may be referred to as a graphics and memorycontroller hub (GMCH).

The input/output controller hub 630 may perform data buffering andinterface arbitration to efficiently operate various system interfaces.The input/output controller hub 630 may be coupled to the input/outputhub 620 via an internal bus, such as a direct media interface (DMI), ahub interface, an enterprise Southbridge interface (ESI), PCIe, etc.

The input/output controller hub 630 may provide various interfaces withperipheral devices. For example, the input/output controller hub 630 mayprovide a universal serial bus (USB) port, a serial advanced technologyattachment (SATA) port, a general purpose input/output (GPIO), a low pincount (LPC) bus, a serial peripheral interface (SPI), PCI, PCIe, etc.

The network device 660 may receive data of the processor 610 and thegraphics card 650 through the PCI express of the input/output hub 620 orone of the USB port, the SATA port, the GPIO, the LPC bus, the SPI, thePCI, and the PCIe. The network device 660 may transmit the data to theother computing system. The network device 660 may receive other datafrom the other computing system.

In some embodiments, the processor 610, the input/output hub 620 and theinput/output controller hub 630 may be implemented as separate chipsetsor separate integrated circuits. In other embodiments, at least two ofthe processor 610, the input/output hub 620 and the input/outputcontroller hub 630 may be implemented as a single chipset.

The foregoing is illustrative of example embodiments and is not to beconstrued as limiting thereof. Although a few example embodiments havebeen described, those skilled in the art will readily appreciate thatmany modifications are possible in the example embodiments withoutmaterially departing from the novel teachings and advantages of thepresent inventive concept. Accordingly, all such modifications areintended to be included within the scope of the present inventiveconcept as defined in the claims. Therefore, it is to be understood thatthe foregoing is illustrative of various example embodiments and is notto be construed as limited to the specific example embodimentsdisclosed, and that modifications to the disclosed example embodiments,as well as other example embodiments, are intended to be included withinthe scope of the appended claims.

What is claimed is:
 1. A user adaptive image compensator, comprising: afeature extractor configured to extract features from an input image; acompensated image generator to receive the input image and configured togenerate compensated preference parameters based on a preferenceparameter, the compensated image generator configured to generate aplurality of compensated images of the input image based on thegenerated compensated preference parameters; an image selectorconfigured to display the plurality of compensated images to a user,receive from the user a selection input for a selected compensatedimage, and generate in response to the selection input the selectedcompensated image as an output image, the image selector being furtherconfigured to output a selected compensated preference parameter thatcorresponds to the selected compensated image; and a preferenceparameter updater coupled to the image selector and the featureextractor, the preference parameter updater being configured to updatethe preference parameter based on the selected compensated preferenceparameter and the extracted features received from the featureextractor, the preference parameter updater being further configured tooutput the preference parameter to the compensated image generator,wherein the plurality of the compensated images include first through(2N+1)-th compensated images, in which N is a natural number, and thecompensated preference parameters include first through (2N+1)-thcompensated preference parameters, wherein the compensated imagegenerator generates the (N+1)-th compensated preference parameter havinga value of the preference parameter respectively corresponding to a(N+1)-th compensation curve, wherein the compensated image generatorgenerates a first through an N-th compensated preference parameterscorresponding to a first through an N-th compensation curves and an(N+2)-th through an (2N+1)-th compensated preference parameterscorresponding to the (N+2)-th through (2N+1)-th compensation curvesbased on the preference parameter, and wherein the compensated imagegenerator generates the first through (2N+1)-th compensated images byrespectively applying the first through (2N+1)-th compensation curves tothe input image.
 2. The user adaptive image compensator of claim 1,wherein a K-th dynamic range, in which K is a natural number equal to orless than (2N+1), is a ratio of a maximum intensity to a minimumintensity of data included in the K-th compensated image, and whereinthe K-th dynamic range is proportional to K.
 3. The user adaptive imagecompensator of claim 1, wherein a K-th compensated image, in which K isa natural number equal to or less than N, is further compensated to ablack color according to the K-th compensation curve in comparison tothe (N+1)-th compensated image as K decreases, and wherein an L-thcompensated image, in which L is a natural number that is equal to orgreater than (N+2) and that is equal to or less than (2N+1), is furthercompensated to a white color according to the L-th compensation curve incomparison to the (N+1)-th compensation image as L increases.
 4. Theuser adaptive image compensator of claim 1, wherein if the user selectsa K-th compensated image, in which K is a natural number equal to orless than (2N+1), the image selector outputs a compensated preferenceparameter corresponding to the K-th compensation curve as the selectedcompensated preference parameter, and the preference parameter updaterupdates the preference parameter as a value generated by combining thepreference parameter and the selected compensated preference parameterbased on a value of a learning speed α.
 5. The user adaptive imagecompensator of claim 1, wherein the preference parameter updaterincludes a preference parameter table, wherein the preference parametertable includes a plurality of content entries corresponding tocombinations of features that may be extracted from the input image asindices, and wherein the preference parameter table stores contentpreference parameters corresponding to the extracted features.
 6. Theuser adaptive image compensator of claim 1, wherein a type and a numberof the extracted features vary according to the input image.
 7. The useradaptive image compensator of claim 1, wherein the extracted featuresare features of a portion of the input image, or are features based onan entirety of the input image.
 8. The user adaptive image compensatorof claim 1, wherein the preference parameter includes vectorsrepresenting a compensation curve describing a preference of the user.9. A user adaptive image compensator, comprising: a feature extractorconfigured to extract features from an input image; a compensated imagegenerator to receive the input image and configured to generatecompensated preference parameters based on an identification signal of acurrent user among a plurality of users and a preference parametercorresponding the extracted features, the compensated image generatorconfigured to generate a plurality of compensated images by compensatingthe input image based on the compensated preference parameters; an imageselector configured to display the plurality of compensated images tothe current user, receive from the current user a selection input for aselected compensated image, and generate in response to the selectioninput the selected compensated image as an output image, the imageselector being further configured to output a selected compensatedpreference parameter from the compensated preference parameters and thatcorresponds to the selected compensated image; and a preferenceparameter updater coupled to the image selector and the featureextractor, the preference parameter updater being configured to updatethe preference parameter based on the selected compensated preferenceparameter, the extracted features received from the feature extractor,and the identification signal, the preference parameter updater beingfurther configured to output the preference parameter to the compensatedimage generator, wherein the plurality of the compensated images includefirst through (2N+1)-th compensated images, in which N is a naturalnumber, and the compensated preference parameters include first through(2N+1)-th compensated preference parameters, wherein the compensatedimage generator generates the (N+1)-th compensated preference parameterhaving a value of the preference parameter respectively corresponding toa (N+1)-th compensation curve, wherein the compensated image generatorgenerates a first through an N-th compensated preference parameterscorresponding to a first through an N-th compensation curves and an(N+2)-th through an (2N+1)-th compensated preference parameterscorresponding to the (N+2)-th through (2N+1)-th compensation curvesbased on the preference parameter, and wherein the compensated imagegenerator generates the first through (2N+1)-th compensated images byrespectively applying the first through (2N+1)-th compensation curves tothe input image.
 10. A user adaptive image compensator, comprising: afeature extractor to extract features from an input image; a compensatedimage generator to receive the input image and to generate at least onecompensated preference parameter based on a current preferenceparameter, the compensated image generator generating a plurality ofcompensated images of the input image based on the at least onegenerated compensated preference parameter; an image selector to displaythe plurality of compensated images, receive from a user a selectioninput for a selected compensated image, and generate in response to theselection input the selected compensated image as an output image fromthe plurality of displayed compensated images, the image selector beingfurther to output a compensated preference parameter that corresponds tothe selected compensated image; and a preference parameter updatercoupled to the image selector and the feature extractor, the preferenceparameter updater being to update the current preference parameter basedon the extracted features received from the feature extractor and thecompensated preference parameter that corresponds to the selectedcompensated image received from the image selector, the preferenceparameter updater being further configured to output the currentpreference parameter to the compensated image generator, wherein theplurality of the compensated images include first through (2N+1)-thcompensated images, in which N is a natural number, wherein thecompensated preference parameters include first through (2N+1)-thcompensated preference parameters, wherein the compensated imagegenerator is to further generate the (N+1)-th compensated preferenceparameter having a value of the preference parameter corresponding to a(N+1)-th compensation curve, wherein the compensated image generator isto further generate a first through an N-th compensated preferenceparameters corresponding to a first through an N-th compensation curvesand an (N+2)-th through an (2N+1)-th compensated preference parametersrespectively corresponding to the (N+2)-th through (2N+1)-thcompensation curves based on the preference parameter, and wherein thecompensated image generator is to further generate the first through(2N+1)-th compensated images by respectively applying the first through(2N+1)-th compensation curves to the input image.
 11. The user adaptiveimage compensator of claim 10, wherein a K-th dynamic range, in which Kis a natural number equal to or less than (2N+1), is a ratio of amaximum intensity to a minimum intensity of data included in the K-thcompensated image, and wherein the K-th dynamic range is proportional toK.
 12. The user adaptive image compensator of claim 10, wherein a K-thcompensated image, in which K is a natural number equal to or less thanN, is further compensated to a black color according to the K-thcompensation curve in comparison to the (N+1)-th compensated image as Kdecreases, and wherein an L-th compensated image, in which L is anatural number that is equal to or greater than (N+2) and that is equalto or less than (2N+1), is further compensated to a white coloraccording to the L-th compensation curve compared to the (N+1)-thcompensation image as L increases.
 13. The user adaptive imagecompensator of claim 10, wherein if the user selects a K-th compensatedimage, in which K is a natural number equal to or less than (2N+1), theimage selector is to further output a compensated preference parametercorresponding to the K-th compensation curve as a selected compensatedpreference parameter, and the preference parameter updater is to furtherupdate the current preference parameter to be a value generated bycombining the preference parameter and the selected compensatedpreference parameter based on a learning speed value a.
 14. The useradaptive image compensator of claim 10, wherein the preference parameterupdater includes a preference parameter table, wherein the preferenceparameter table includes a plurality of content entries corresponding tocombinations of features that may be extracted from the input image asindices, and wherein the preference parameter table is to further storecontent preference parameters corresponding to the extracted features.15. The user adaptive image compensator of claim 14, wherein thepreference parameter updater is to further store the updated preferenceparameter as a first content preference parameter of a first contententry corresponding to the extracted features in the preferenceparameter table, and wherein the preference parameter updater is tofurther read the first content preference parameter corresponding to theextracted features from the preference parameter table and output thefirst content preference parameter as the current preference parameter.16. The user adaptive image compensator of claim 10, wherein a type anda number of the extracted features vary according to the input image.17. The user adaptive image compensator of claim 10, wherein theextracted features are features of a portion of the input image, or arefeatures based on an entirety of the input image.
 18. The user adaptiveimage compensator of claim 10, wherein the preference parameter includesvectors representing a compensation curve that is based on a preferenceof the user.